Federated Learning for Privacy-Preserving AI in Healthcare Applications
Abstract
Data privacy concerns in healthcare often limit the availability of centralized datasets for AI model training. This paper introduces a federated learning framework that enables collaborative model training across multiple healthcare institutions without sharing sensitive patient data. The proposed system ensures privacy by keeping data local while aggregating model updates securely. Experiments on medical imaging and electronic health record datasets demonstrate that federated learning achieves comparable performance to centralized training while preserving privacy. This research underscores the potential of federated learning to drive innovation in healthcare AI while addressing ethical and regulatory challenges.
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